Professor Jeff Hong received his PhD in Industrial Engineering and Management Sciences from Northwestern University in 2004, MSc in Applied Mathematics from the University of Cincinnati in 2001, and BEng in Automative Engineering and BEng in Industrial Engineering from Tsinghua University in 1999. He is now the Visiting Professor of NSCIIC.
Prior to joining the City University of Hong Kong, he was Professor, Associate Director of Logistics and Supply Chain Management Institute, and Director of Financial Engineering Laboratory in Department of Industrial Engineering and Logistics Management at the Hong Kong University of Science and Technology. He published extensively on leading academic journals such as Operations Research and Management Science. He was the winner of the 2009 Operations Best Paper Award from the Institute of Industrial Engineers, the 2012 Outstanding Simulation Publication Award from the INFORMS Simulation Society, and the inaugural Outstanding Research Award from the Operational Research Society of China in 2014. He is currently an Associate Editor ofOperations Research, Naval Research Logistics and ACM Transactions on Modeling and Computer Simulation.
Management sciences, operations research, financial engineering and risk management, and business analytics
L. Jeff Hong, SandeepJuneja, and Guangwu Liu. (2017), "Kernel smoothing for nested estimation with application to portfolio risk measurement", Operations Research, 65, 3, 657-673.
Weiwei Fan, L. Jeff Hong, and Barry L. Nelson (2016), "Indifference-zone-free selection of the best", Operations Research, 64, 1499-1514.
L. Jeff Hong, Xiaowei Xu, and Shenghao Zhang (2015), "Capacity reservation for time-sensitive service providers: An application in seaport management", European Journal of Operational Research, 245, 490-490.
L. Jeff Hong, Jun Luo, and Barry L. Nelson (2015), "Chance constrained selection of the best", Informs Journal on Computing, 27, 317-334.
Jun Luo, L. Jeff Hong, Barry L. Nelson, and Yang Wu (2015), "Fully sequential procedures for large-scale ranking-and-selection problems in parallel computing environments.", Operations Research, 63, 1177-1194.
L. Jeff Hong, Zhaolin Hu, and Guangwu Liu (2014), "Monte Carlo methods for value-at-risk and conditional value-at-risk: A review", ACM Transactions on Modeling and Computer Simulation, 24, 22/1-22/37.